4,084 research outputs found
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
Fusion of Learned Multi-Modal Representations and Dense Trajectories for Emotional Analysis in Videos
When designing a video affective content analysis algorithm, one of the most important steps is the selection of discriminative features for the effective representation of video segments. The majority of existing affective content analysis methods either use low-level audio-visual features or generate handcrafted higher level representations based on these low-level features. We propose in this work to use deep learning methods, in particular convolutional neural networks (CNNs), in order to automatically learn and extract mid-level representations from raw data. To this end, we exploit the audio and visual modality of videos by employing Mel-Frequency Cepstral Coefficients (MFCC) and color values in the HSV color space. We also incorporate dense trajectory based motion features in order to further enhance the performance of the analysis. By means of multi-class support vector machines (SVMs) and fusion mechanisms, music video clips are classified into one of four affective categories representing the four quadrants of the Valence-Arousal (VA) space. Results obtained on a subset of the DEAP dataset show (1) that higher level representations perform better than low-level features, and (2) that incorporating motion information leads to a notable performance gain, independently from the chosen representation
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Music emotion recognition: a multimodal machine learning approach
Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major selection preferred by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for innovative and adaptable search mechanisms, which can be personalized according to users’ emotional state, has gained increasing consideration in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semisupervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the crossvalidated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performanc
NBP 2.0: Updated Next Bar Predictor, an Improved Algorithmic Music Generator
Deep neural network advancements have enabled machines to produce melodies emulating human-composed music. However, the implementation of such machines is costly in terms of resources. In this paper, we present NBP 2.0, a refinement of the previous model next bar predictor (NBP) with two notable improvements: first, transforming each training instance to anchor all the notes to its musical scale, and second, changing the model architecture itself. NBP 2.0 maintained its straightforward and lightweight implementation, which is an advantage over the baseline models. Improvements were assessed using quantitative and qualitative metrics and, based on the results, the improvements from these changes made are notable
Automatic Personalized Playlist Generation
Käesolevas magistritöös on esitatud automaatse personaliseeritud pleilisti tekitaja probleemi lähenemisviiside uuring. Lisaks teoreetilise tausta lühiülevaatele me dokumenteerisime oma lähenemist: meie poolt tehtud katsed ning nende tulemused. Meie algoritm koosneb kahest põhiosast: pleilisti hindamisfunktsiooni konstrueerimine ning pleilisti genereerimisstrateegia valik. Esimese ülesande lahendamiseks on valitud Naive Bayes klassifitseerija ning 5-elemendiline MIRtoolbox tööristakasti poolt kavandatud audio sisupõhiste attribuutide vektor, mis klassiitseerivad pleilisti heaks või halvaks 82% täpsusega - palju parem kui juhuslik klassifitseerija (50%). Teise probleemi lahendamiseks proovisime kolm genereerimisalgoritmi: lohistus (Shuffle), randomiseeritud otsing (Randomized Search) ning geneetiline algoritm (Genetic Algorithm). Vastavalt katsete tulemustele kõige paremini ja kiiremini töötab randomiseeritud otsingu algoritm. Kõik katsed on tehtud 5 ning 10 elemendilistel pleilistidel.
Kokkuvõttes, oleme arendanud automatiseeritud personaliseeritud pleilisti tekitaja algoritmi, mis vastavalt meie hinnangutele vastab ka kasutaja ootustele rohkem, kui juhuslikud lohistajad. Algoritmi võib kasutada keerulisema pleilistide konstrueerimiseks
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